Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions at single ward level. We adapted the Ross-Macdonald model to describe hospital cross-transmission dynamics of carbapenem resistant Klebsiella pneumoniae (CRKP), considering healthcare workers as the vectors transmitting susceptible and resistant pathogens among admitted patients. The model parameters were estimated from a literature review, further adjusted to reproduce observed clinical outcomes, and validated using real life data from a 2-year study in a university hospital. The model has been further explored through extensive sensitivity analysis, in order to assess the relevance of single interventions as well as their synergistic effects. Our model has been shown to be an effective tool to describe and predict the impact of interventions in reducing the prevalence of CRKP colonisation and infection, and can be extended to other specific hospital and pathological scenarios to produce tailored estimates of the most effective strategies.


Cohorting
Cohorted contacts reduces HCW-patient mixing, by reducing the number of HCWs contributing to transmission [1] .For example, one-to-one nursing by a fraction of HCWs (H = HF+HS+HR) corresponds to an effective reduction H(1-q) in HCWs number in the model.

Isolation and
pre-emptive isolation HCW-patient mixing can be decreased by reducing the number of daily contacts between HCWs and patients, through the respective parameter KH.

Antibiotic consumption policies
By reducing the antibiotic DOTs or choosing antibiotics with lower risk of selecting resistant strains [1,2] , it is possible to decrease the emergence and spread of resistant strains.In their study, Austin et al antibiotic restriction policies are introduced into the model to simulate reduction in selection pressure (and hence probability of patient colonization) [1] .They estimate that, if antibiotic selection pressure gives an increased relative risk  of acquisition whist the patient is receiving treatment, and patients receive antibiotics for a fraction  of their LOS, then the probability per contact of colonization is increased by a factor of  = 1 + ( − 1).Within our case study, we estimate  from the days of therapy (DOT) per pd of the resistance selecting antibiotics, as: where the average treatment duration is meant to be as if the daily doses observed had been distributed to all the patients admitted, thus it must not be confused with the average treatment duration calculated only on the patients who received an antibiotic treatment.The increased relative risk estimate is  = 3.15 for the preintervention period and  = 2.94 for post-intervention, as the average of the resistance selecting antibiotics increased risks from [6].The average is computed on literature risks weighted on hospital data DOTs of ꞵ-lactam, cephalosporins, carbapenems and fluoroquinolones antibiotics.Antibiotic prescription for the patients was considered as independent from the epidemiological status, in the sense that the DOTs were considered to be the same for each epidemiological compartment (PF, PS, PR).
Hand hygiene Hand hygiene compliance h contributes to the probability of bacterial transmission during the contacts between contaminated and un-colonized individuals [3] .To estimate h, we consider the following equation: In particular, we can estimate: ℎ = #      =    /    In which the recommended single gel dose is 0.004 litres, as indicated in the WHO Guidelines on Hand Hygiene in Health Care.Since we don't know the total amount of gel consumption, we can estimate it from our data as follows: =     *  where the gel consumption per pd is 0.04427 litres per patient-days (pd).Patient days (pd, with values in Supplementary Table S5) are defined as the sum of the LOS of all patients admitted in the observation period (equal to 14382 pd in the pre-intervention period).The total number of contacts C can be estimated as: Where   is the number of daily contacts per HCW per patient (Table 1), H=17 is the HCWs number, P = 0.79 bed occupancy * 46 beds = 36.34 is the average number of patients, and days=399 is the duration of the pre-intervention period.Thus, we can calculate h as follows: In our case study, both h and   must be estimated, but through this relation, only one need to be fitted.

Screening at admission
Universal screening was modelled through the parameter describing the resistance prevalence at admission as it usually results in patient isolation thus decreasing the entry of individuals colonized/infected with resistant strains.The fraction of patients colonized and or infected at admission had been extracted from the hospital data.To simulate the effect of the screening at admission, followed by isolation, we decreased or increased this rate of infected people at admission.

Clinical data (SAVE intervention)
To estimate prevalence of Klebsiella pneumoniae samples collected within the 72 hours from admission and on weekly basis were selected.The samples comprised rectal swabs from screening activities and clinical specimens from different sources collected at the discretion of the attending physicians (e.g.blood, wound swabs, urine, sputum, bronchoalveolar lavage).Patients colonized and/or infected by carbapenem resistant K.pneumoniae (CRKP) were those with a sample positive for carbapenem resistant strain; patients colonized and/or infected by carbapenem susceptible strain (CSKP) were those with a sample holding a negative result for CRKP (e.g.samples of Klebsiella pneumoniae ESBL-producers were considered in this category); uncolonized or "free" patients were defined as those with negative microbiological samples or positive for pathogens other than K.

Length of stay Pre-intervention days (d) Post-intervention days (d)
. A) Variables collected for model validation.AMC: antimicrobial consumption; DDD: defined daily dose; DOT: days of therapy; HCW: health care worker; PF: not colonized/free; PR: colonized/infected by resistant strain; PS: colonized/infected by susceptible strain.B) DOTs per patient-day for the different antibiotic classes.(a) Supplementary Figure S1.CRKP weekly point prevalence over time, plotted both as raw data and as a moving average on 8 periods-weeks with the standard deviation as confidence interval.Dashed lines represent the average resistance prevalence before (light blue) and after (dark blue) the intervention.colonization and/or infection rate after intervention; RaRa: Pooled rate ratio, rate ratio of infections and/or colonisation between standard of care and intervention period; IRaRa: Incidence rate ratio, incidence rate ratio of infections and/or colonisation between standard of care and intervention; IRs: Incidence ratio, ratio between infection/colonisation before and after intervention.